Data in business and similar applications is often viewed in the form of a spreadsheet. A spreadsheet may be thought of as a “two dimensional” array of data. Each cell in the spreadsheet represents a value of two related entities, or dimensions. For example, one dimension may be time, while a corresponding cross-dimension may be revenue. Many applications, however, may have data, which has more than two dimensions. Business data having more than two dimensions are called multi-dimensional data.
Multi-dimensional data may be represented in an Online Analytical Processing (OLAP) model such as Microsoft SQL Server Analysis Service® cube, for performing operations such as allocation, query, and so on in an optimal fashion. OLAP data sources typically contain a time dimension in addition to other dimensions.
OLAP client tools—applications that perform operations on data stored in an OLAP model—typically rely on a standard set of OLAP query language functions to enable analysis of the data within a OLAP data store (e.g. a data cube). The user analysis experience through such a client tool involves a multi-step approach to applying those query language functions to the data and another multi-step approach to selecting the correct visualization for understanding the data. The multi-step approach may be above the level of capability of a typical business user, and may lead to a stunted decision or require specialist capabilities.